Compared methods. We compare the proposed method with following models: (a) MCDO (monte carlo dropout) [1]} casts dropout network training as approximate inference in a Bayesian neural network; (b) DE (deep ensemble) [2]} is a simple, non-Bayesian method which involves training multiple deep models; (c) UA (uncertainty-aware attention) [3]}
generates attention weights following a Gaussian distribution with a learned mean and variance, which allows heteroscedastic uncertainty to be captured and yields a more accurate calibration of prediction uncertainty; (d) EDL (evidential deep Learning) [4]} designs a predictive distribution for classification by placing a Dirichlet distribution on the class probabilities.
